A surveillance system essentially involves a moving object detection technique, which is intended to allow the system to more accurately and rapidly detect a moving object. Subsequent operations such as tracking, identification and analysis procedures on the moving object are highly dependent on the accuracy of moving object detection. The accuracy of moving object detection may even be decisive on the reliability and accuracy of the entire system, and is thus a crucial factor for evaluating the quality of a surveillance system.
Numerous publications on the object detection realm have been disclosed. Among the researches, three methods, namely an optical flow method, a frame difference method and a background subtraction method, are most prevalent. In the optical flow method, a motion vector in an image in consecutive frames is identified and characteristic matching is then performed, and is applicable to motion detection and moving object segmentation. Although the optical flow method is extremely effective in applications including pattern recognition and computer vision as well as other image processing applications, the optical flow method falls short in providing a real-time effect due to a high sensitivity on noises in a scene and a huge computation amount resulted by complex algorithm.
In the frame subtraction method, every two pixel values in consecutive frames are subtracted to obtain a difference. The pixel is considered as the background when the difference is smaller than a threshold, or the pixel is considered as a part of a moving object when a large variance exists in the pixel and the difference is greater than the threshold. This method, being quite simple and fast, however frequently obtains only borders of a moving object rather than a complete object.
In the background subtraction method, a background model is first established. The background model is compared with a new image to further obtain a moving object. This method is both simple and fast. Although the concept of the background subtraction method may seem easy, the background subtraction method suffers from many challenging issues. For example, results of segmentation are liable to errors including fragments, high noises and segmentation failures. To prevent the above errors, updating and display capabilities of the background need to be reinforced, which may on the other hand lead to a tremendous load on the system memory and computation performance.